July 2023
Volume 23, Issue 7
Open Access
Erratum  |   July 2023
Corrections to: An objective method for measuring face detection thresholds using the sweep steady-state visual evoked response
Journal of Vision July 2023, Vol.23, 16. doi:https://doi.org/10.1167/jov.23.7.16
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      Corrections to: An objective method for measuring face detection thresholds using the sweep steady-state visual evoked response. Journal of Vision 2023;23(7):16. https://doi.org/10.1167/jov.23.7.16.

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      © ARVO (1962-2015); The Authors (2016-present)

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CORRECTIONS TO: Ales, J. M., Farzin, F., Rossion, B., & Norcia, A. M. (2012). An objective method for measuring face detection thresholds using the sweep steady-state visual evoked response. Journal of Vision, 12(10):18, 1–18, https://doi.org/10.1167/jov.12.10.18
The authors have noted two errors in Figures 13 and 14. The first error is a tabulation error leading to incorrect data being plotted and used for corresponding correlations. The second is that data were shown for a recording channel (91) that was not used in any of the other figures (they all used channel 96). Figures 13 and 14 and some corresponding text have been corrected in the article online. 
Original Figures 13 and 14 and legends: 
Figure 13.
 
Correlation between ssVEP (channel 91) and psychophysical face detection thresholds for each face exemplar. Each data point represents the average of 10 participants. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 13.
 
Correlation between ssVEP (channel 91) and psychophysical face detection thresholds for each face exemplar. Each data point represents the average of 10 participants. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 14.
 
Correlation between ssVEP (channel 91) and psychophysical face detection thresholds for each participant. Each data point represents the average of 15 face exemplars. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 14.
 
Correlation between ssVEP (channel 91) and psychophysical face detection thresholds for each participant. Each data point represents the average of 15 face exemplars. The best fitting two-parameter (slope and offset) line to the data is shown.
Corrected Figures 13 and 14 and legends: 
Figure 13.
 
Correlation between ssVEP (channel 96) and psychophysical face detection thresholds for each face exemplar. Each data point represents the average of 10 participants. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 13.
 
Correlation between ssVEP (channel 96) and psychophysical face detection thresholds for each face exemplar. Each data point represents the average of 10 participants. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 14.
 
Correlation between ssVEP (channel 96) and psychophysical face detection thresholds for each participant. Each data point represents the average of 15 face exemplars. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 14.
 
Correlation between ssVEP (channel 96) and psychophysical face detection thresholds for each participant. Each data point represents the average of 15 face exemplars. The best fitting two-parameter (slope and offset) line to the data is shown.
Original text of the last paragraph of the Results: 
Figure 13 shows the correlation between ssVEP thresholds derived as described above and psychophysical thresholds for each face exemplar. The ssVEP and psychophysical face detection thresholds were significantly correlated (R2 = 0.93; p < 1e-8). Figure 14 presents the same comparison across participants and here the correlation was also significant (R2 = 0.86, p < 0.001). The slopes of the regression lines were both close to 1, indicating a 1:1 relationship between ssVEP and perceptual sensitivity. 
Corrected text: 
Figure 13 shows the correlation between ssVEP thresholds derived as described above and behavioral thresholds for each face exemplar. The ssVEP and psychophysical face detection thresholds were significantly correlated (Pearson's r = 0.52; p = 0.024), one-tailed. One of the face exemplars failed to yield a threshold (point plotted at VEP coherence level of 100), so we also calculated the more robust Kendall's tau correlation which was also significant (tau = 0.45; p = 0.012, one tailed). Figure 14 presents the same comparison across participants and here the correlations were also significant (r = 0.7829; p = 0.0037; tau = 0.689; p = 0.0038). 
The slope of the regression line for the face image correlation was 0.89, indicating a close relationship between the absolute thresholds, but not for the subject correlations where the slope was 2.9. 
Figure 13.
 
Correlation between ssVEP (channel 91) and psychophysical face detection thresholds for each face exemplar. Each data point represents the average of 10 participants. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 13.
 
Correlation between ssVEP (channel 91) and psychophysical face detection thresholds for each face exemplar. Each data point represents the average of 10 participants. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 14.
 
Correlation between ssVEP (channel 91) and psychophysical face detection thresholds for each participant. Each data point represents the average of 15 face exemplars. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 14.
 
Correlation between ssVEP (channel 91) and psychophysical face detection thresholds for each participant. Each data point represents the average of 15 face exemplars. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 13.
 
Correlation between ssVEP (channel 96) and psychophysical face detection thresholds for each face exemplar. Each data point represents the average of 10 participants. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 13.
 
Correlation between ssVEP (channel 96) and psychophysical face detection thresholds for each face exemplar. Each data point represents the average of 10 participants. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 14.
 
Correlation between ssVEP (channel 96) and psychophysical face detection thresholds for each participant. Each data point represents the average of 15 face exemplars. The best fitting two-parameter (slope and offset) line to the data is shown.
Figure 14.
 
Correlation between ssVEP (channel 96) and psychophysical face detection thresholds for each participant. Each data point represents the average of 15 face exemplars. The best fitting two-parameter (slope and offset) line to the data is shown.
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